{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "137dd095",
   "metadata": {},
   "source": [
    "Explorations of `duration-prediction.ipynb` from Module 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b2bd82d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.9.12\n"
     ]
    }
   ],
   "source": [
    "!python -V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "41062d8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c984c564",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4add538c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b135c2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1464985f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023/05/11 11:51:37 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
      "2023/05/11 11:51:37 INFO mlflow.store.db.utils: Updating database tables\n",
      "INFO  [alembic.runtime.migration] Context impl SQLiteImpl.\n",
      "INFO  [alembic.runtime.migration] Will assume non-transactional DDL.\n",
      "INFO  [alembic.runtime.migration] Running upgrade  -> 451aebb31d03, add metric step\n",
      "INFO  [alembic.runtime.migration] Running upgrade 451aebb31d03 -> 90e64c465722, migrate user column to tags\n",
      "INFO  [alembic.runtime.migration] Running upgrade 90e64c465722 -> 181f10493468, allow nulls for metric values\n",
      "INFO  [alembic.runtime.migration] Running upgrade 181f10493468 -> df50e92ffc5e, Add Experiment Tags Table\n",
      "INFO  [alembic.runtime.migration] Running upgrade df50e92ffc5e -> 7ac759974ad8, Update run tags with larger limit\n",
      "INFO  [alembic.runtime.migration] Running upgrade 7ac759974ad8 -> 89d4b8295536, create latest metrics table\n",
      "INFO  [89d4b8295536_create_latest_metrics_table_py] Migration complete!\n",
      "INFO  [alembic.runtime.migration] Running upgrade 89d4b8295536 -> 2b4d017a5e9b, add model registry tables to db\n",
      "INFO  [2b4d017a5e9b_add_model_registry_tables_to_db_py] Adding registered_models and model_versions tables to database.\n",
      "INFO  [2b4d017a5e9b_add_model_registry_tables_to_db_py] Migration complete!\n",
      "INFO  [alembic.runtime.migration] Running upgrade 2b4d017a5e9b -> cfd24bdc0731, Update run status constraint with killed\n",
      "INFO  [alembic.runtime.migration] Running upgrade cfd24bdc0731 -> 0a8213491aaa, drop_duplicate_killed_constraint\n",
      "INFO  [alembic.runtime.migration] Running upgrade 0a8213491aaa -> 728d730b5ebd, add registered model tags table\n",
      "INFO  [alembic.runtime.migration] Running upgrade 728d730b5ebd -> 27a6a02d2cf1, add model version tags table\n",
      "INFO  [alembic.runtime.migration] Running upgrade 27a6a02d2cf1 -> 84291f40a231, add run_link to model_version\n",
      "INFO  [alembic.runtime.migration] Running upgrade 84291f40a231 -> a8c4a736bde6, allow nulls for run_id\n",
      "INFO  [alembic.runtime.migration] Running upgrade a8c4a736bde6 -> 39d1c3be5f05, add_is_nan_constraint_for_metrics_tables_if_necessary\n",
      "INFO  [alembic.runtime.migration] Running upgrade 39d1c3be5f05 -> c48cb773bb87, reset_default_value_for_is_nan_in_metrics_table_for_mysql\n",
      "INFO  [alembic.runtime.migration] Running upgrade c48cb773bb87 -> bd07f7e963c5, create index on run_uuid\n",
      "INFO  [alembic.runtime.migration] Running upgrade bd07f7e963c5 -> 0c779009ac13, add deleted_time field to runs table\n",
      "INFO  [alembic.runtime.migration] Running upgrade 0c779009ac13 -> cc1f77228345, change param value length to 500\n",
      "INFO  [alembic.runtime.migration] Running upgrade cc1f77228345 -> 97727af70f4d, Add creation_time and last_update_time to experiments table\n",
      "INFO  [alembic.runtime.migration] Running upgrade 97727af70f4d -> 3500859a5d39, Add Model Aliases table\n",
      "INFO  [alembic.runtime.migration] Context impl SQLiteImpl.\n",
      "INFO  [alembic.runtime.migration] Will assume non-transactional DDL.\n",
      "2023/05/11 11:51:37 INFO mlflow.tracking.fluent: Experiment with name 'nyc-taxi-experiment' does not exist. Creating a new experiment.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Experiment: artifact_location='/Users/jeffhale/Desktop/prefect/zoom_mlops/3.3/mlruns/1', creation_time=1683820297414, experiment_id='1', last_update_time=1683820297414, lifecycle_stage='active', name='nyc-taxi-experiment', tags={}>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mlflow\n",
    "\n",
    "\n",
    "mlflow.set_tracking_uri(\"sqlite:///mlflow.db\")\n",
    "mlflow.set_experiment(\"nyc-taxi-experiment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f9e6479e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_dataframe(filename):\n",
    "    df = pd.read_parquet(filename) # switched to reading in parquet files\n",
    "\n",
    "    df.lpep_dropoff_datetime = pd.to_datetime(df.lpep_dropoff_datetime)\n",
    "    df.lpep_pickup_datetime = pd.to_datetime(df.lpep_pickup_datetime)\n",
    "\n",
    "    df['duration'] = df.lpep_dropoff_datetime - df.lpep_pickup_datetime\n",
    "    df.duration = df.duration.apply(lambda td: td.total_seconds() / 60)\n",
    "\n",
    "    df = df[(df.duration >= 1) & (df.duration <= 60)]\n",
    "\n",
    "    categorical = ['PULocationID', 'DOLocationID']\n",
    "    df[categorical] = df[categorical].astype(str)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8029eba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = read_dataframe(filename=\"../data/green_tripdata_2021-01.parquet\")\n",
    "df_val = read_dataframe(filename=\"../data/green_tripdata_2021-02.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f6f2f0eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73908, 61921)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train), len(df_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "951d51ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['PU_DO'] = df_train['PULocationID'] + '_' + df_train['DOLocationID']\n",
    "df_val['PU_DO'] = df_val['PULocationID'] + '_' + df_val['DOLocationID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c5cbfc25",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = ['PU_DO'] #'PULocationID', 'DOLocationID']\n",
    "numerical = ['trip_distance']\n",
    "\n",
    "dv = DictVectorizer()\n",
    "\n",
    "train_dicts = df_train[categorical + numerical].to_dict(orient='records')\n",
    "X_train = dv.fit_transform(train_dicts)\n",
    "\n",
    "val_dicts = df_val[categorical + numerical].to_dict(orient='records')\n",
    "X_val = dv.transform(val_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0b2eea4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scipy.sparse._csr.csr_matrix"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train) # sparse matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4aeee3f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scipy.sparse._csr.csr_matrix"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1e9fb68b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if models can handle sparse arrays, don't need to do this Jeff says\n",
    "target = 'duration'\n",
    "y_train = df_train[target].values\n",
    "y_val = df_val[target].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "296c74b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0c9ab534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "429e2394",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.758715206931833"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(X_val)\n",
    "\n",
    "mean_squared_error(y_val, y_pred, squared=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "22bf6f8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# jeff added pathlib making directory\n",
    "import pathlib\n",
    "pathlib.Path(\"models\").mkdir(exist_ok=True)\n",
    "     \n",
    "\n",
    "with open('models/lin_reg2.bin', 'wb') as f_out:\n",
    "    pickle.dump((dv, lr), f_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a4999b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "with mlflow.start_run():\n",
    "\n",
    "    mlflow.set_tag(\"developer\", \"cristian\")\n",
    "\n",
    "    mlflow.log_param(\"train-data-path\", \"./data/green_tripdata_2021-01.csv\")\n",
    "    mlflow.log_param(\"valid-data-path\", \"./data/green_tripdata_2021-02.csv\")\n",
    "\n",
    "    alpha = 0.1\n",
    "    mlflow.log_param(\"alpha\", alpha)\n",
    "    lr = Lasso(alpha)\n",
    "    lr.fit(X_train, y_train)\n",
    "\n",
    "    y_pred = lr.predict(X_val)\n",
    "    rmse = mean_squared_error(y_val, y_pred, squared=False)\n",
    "    mlflow.log_metric(\"rmse\", rmse)\n",
    "\n",
    "    mlflow.log_artifact(local_path=\"models/lin_reg.bin\", artifact_path=\"models_pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5f56e97b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "907dc6a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n",
    "from hyperopt.pyll import scope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "091ab328",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = xgb.DMatrix(X_train, label=y_train)\n",
    "valid = xgb.DMatrix(X_val, label=y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c2d84b26",
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(params):\n",
    "    with mlflow.start_run():\n",
    "        mlflow.set_tag(\"model\", \"xgboost\")\n",
    "        mlflow.log_params(params)\n",
    "        booster = xgb.train(\n",
    "            params=params,\n",
    "            dtrain=train,\n",
    "            num_boost_round=1000,\n",
    "            evals=[(valid, 'validation')],\n",
    "            early_stopping_rounds=50\n",
    "        )\n",
    "        y_pred = booster.predict(valid)\n",
    "        rmse = mean_squared_error(y_val, y_pred, squared=False)\n",
    "        mlflow.log_metric(\"rmse\", rmse)\n",
    "\n",
    "    return {'loss': rmse, 'status': STATUS_OK}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6b1f77f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:38:06] WARNING: /Users/runner/work/xgboost/xgboost/python-package/build/temp.macosx-11.0-arm64-3.8/xgboost/src/objective/regression_obj.cu:203: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[0]\tvalidation-rmse:11.27920                          \n",
      "[1]\tvalidation-rmse:8.01759                           \n",
      "[2]\tvalidation-rmse:7.08526                           \n",
      "[3]\tvalidation-rmse:6.80494                           \n",
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      "[12:46:29] WARNING: /Users/runner/work/xgboost/xgboost/python-package/build/temp.macosx-11.0-arm64-3.8/xgboost/src/objective/regression_obj.cu:203: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[0]\tvalidation-rmse:11.01936                                                      \n",
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      "[12:49:30] WARNING: /Users/runner/work/xgboost/xgboost/python-package/build/temp.macosx-11.0-arm64-3.8/xgboost/src/objective/regression_obj.cu:203: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[0]\tvalidation-rmse:9.30271                                                       \n",
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      "[687]\tvalidation-rmse:6.46949                                                   \n",
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      "[710]\tvalidation-rmse:6.46965                                                   \n",
      "[711]\tvalidation-rmse:6.46963                                                   \n",
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      "[713]\tvalidation-rmse:6.46946                                                   \n",
      "[714]\tvalidation-rmse:6.46950                                                   \n",
      "[715]\tvalidation-rmse:6.46953                                                   \n",
      "[716]\tvalidation-rmse:6.47009                                                   \n",
      "[717]\tvalidation-rmse:6.47049                                                   \n",
      "[718]\tvalidation-rmse:6.47060                                                   \n",
      "[719]\tvalidation-rmse:6.47058                                                   \n",
      "[720]\tvalidation-rmse:6.47056                                                   \n",
      "[721]\tvalidation-rmse:6.47059                                                   \n",
      "[722]\tvalidation-rmse:6.47061                                                   \n",
      "[723]\tvalidation-rmse:6.47063                                                   \n",
      "[724]\tvalidation-rmse:6.47067                                                   \n",
      "[725]\tvalidation-rmse:6.47065                                                   \n",
      "[726]\tvalidation-rmse:6.47056                                                   \n",
      "[727]\tvalidation-rmse:6.47060                                                   \n",
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      "[729]\tvalidation-rmse:6.47065                                                   \n",
      "[730]\tvalidation-rmse:6.47061                                                   \n",
      "[731]\tvalidation-rmse:6.47060                                                   \n",
      "[732]\tvalidation-rmse:6.47059                                                   \n",
      "[733]\tvalidation-rmse:6.47050                                                   \n",
      "[734]\tvalidation-rmse:6.47049                                                   \n",
      "[735]\tvalidation-rmse:6.47057                                                   \n",
      " 36%|███▌      | 18/50 [23:33<23:33, 44.17s/trial, best loss: 6.321673211235433]"
     ]
    }
   ],
   "source": [
    "search_space = {\n",
    "    'max_depth': scope.int(hp.quniform('max_depth', 4, 100, 1)),\n",
    "    'learning_rate': hp.loguniform('learning_rate', -3, 0),\n",
    "    'reg_alpha': hp.loguniform('reg_alpha', -5, -1),\n",
    "    'reg_lambda': hp.loguniform('reg_lambda', -6, -1),\n",
    "    'min_child_weight': hp.loguniform('min_child_weight', -1, 3),\n",
    "    'objective': 'reg:linear',\n",
    "    'seed': 42\n",
    "}\n",
    "\n",
    "best_result = fmin(\n",
    "    fn=objective,\n",
    "    space=search_space,\n",
    "    algo=tpe.suggest,\n",
    "    max_evals=50,\n",
    "    trials=Trials()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fc4d5283",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlflow.xgboost.autolog(disable=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0e8cd729",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "with mlflow.start_run():\n",
    "    \n",
    "    train = xgb.DMatrix(X_train, label=y_train)\n",
    "    valid = xgb.DMatrix(X_val, label=y_val)\n",
    "\n",
    "    best_params = {\n",
    "        'learning_rate': 0.09585355369315604,\n",
    "        'max_depth': 30,\n",
    "        'min_child_weight': 1.060597050922164,\n",
    "        'objective': 'reg:linear',\n",
    "        'reg_alpha': 0.018060244040060163,\n",
    "        'reg_lambda': 0.011658731377413597,\n",
    "        'seed': 42\n",
    "    }\n",
    "\n",
    "    mlflow.log_params(best_params)\n",
    "\n",
    "    booster = xgb.train(\n",
    "        params=best_params,\n",
    "        dtrain=train,\n",
    "        num_boost_round=1000,\n",
    "        evals=[(valid, 'validation')],\n",
    "        early_stopping_rounds=50\n",
    "    )\n",
    "\n",
    "    y_pred = booster.predict(valid)\n",
    "    rmse = mean_squared_error(y_val, y_pred, squared=False)\n",
    "    mlflow.log_metric(\"rmse\", rmse)\n",
    "\n",
    "    with open(\"models/preprocessor.b\", \"wb\") as f_out:\n",
    "        pickle.dump(dv, f_out)\n",
    "    mlflow.log_artifact(\"models/preprocessor.b\", artifact_path=\"preprocessor\")\n",
    "\n",
    "    mlflow.xgboost.log_model(booster, artifact_path=\"models_mlflow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "87614b82",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cristian.martinez/miniconda3/envs/exp-tracking-env/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor\n",
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "mlflow.sklearn.autolog()\n",
    "\n",
    "for model_class in (RandomForestRegressor, GradientBoostingRegressor, ExtraTreesRegressor, LinearSVR):\n",
    "\n",
    "    with mlflow.start_run():\n",
    "\n",
    "        mlflow.log_param(\"train-data-path\", \"./data/green_tripdata_2021-01.csv\")\n",
    "        mlflow.log_param(\"valid-data-path\", \"./data/green_tripdata_2021-02.csv\")\n",
    "        mlflow.log_artifact(\"models/preprocessor.b\", artifact_path=\"preprocessor\")\n",
    "\n",
    "        mlmodel = model_class()\n",
    "        mlmodel.fit(X_train, y_train)\n",
    "\n",
    "        y_pred = mlmodel.predict(X_val)\n",
    "        rmse = mean_squared_error(y_val, y_pred, squared=False)\n",
    "        mlflow.log_metric(\"rmse\", rmse)\n",
    "        "
   ]
  }
 ],
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